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Synthetic Data Generation Jobs (NOW HIRING)

Senior Software Engineer - Simulation

Santa Clara, CA · On-site

$143K - $189K/yr

They are seeking a highly motivated and experienced Senior Software Engineer to join their Metropolis Synthetic Data Generation team to build scalable Physical AI Digital Twin and Synthetic Data ...

Platform Engineer, Data

Austin, TX · On-site

$113K - $136K/yr

You'll partner closely with our ML engineers to orchestrate ingestion, synthetic data generation, and versioned releases, ensuring that every dataset is not only high-integrity and available but ...

Platform Engineer, Data

Austin, TX · On-site

$113K - $136K/yr

You'll partner closely with our ML engineers to orchestrate ingestion, synthetic data generation, and versioned releases, ensuring that every dataset is not only high-integrity and available but ...

Platform Engineer, Data

Austin, TX · On-site

$113K - $136K/yr

You'll partner closely with our ML engineers to orchestrate ingestion, synthetic data generation, and versioned releases, ensuring that every dataset is not only high-integrity and available but ...

Platform Engineer, Data

Austin, TX · On-site

$113K - $136K/yr

You'll partner closely with our ML engineers to orchestrate ingestion, synthetic data generation, and versioned releases, ensuring that every dataset is not only high-integrity and available but ...

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Synthetic Data Generation information

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$31K

$93.2K

$169K

How much do synthetic data generation jobs pay per year?

As of Jul 5, 2026, the average yearly pay for synthetic data generation in the United States is $93,198.00, according to ZipRecruiter salary data. Most workers in this role earn between $54,500.00 and $144,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in a Synthetic Data Generation role, and why are they important?

To excel in a Synthetic Data Generation role, you need a solid background in computer science, statistics, and data science, often supported by a relevant degree and experience in machine learning. Familiarity with tools such as Python, TensorFlow, PyTorch, and synthetic data generation platforms, as well as knowledge of privacy-preserving techniques, is typically required. Strong problem-solving abilities, creativity, and effective communication set top performers apart in this field. These skills and qualities are crucial for creating high-quality, realistic synthetic datasets that support robust AI model development while safeguarding sensitive information.

What is the salary of a synthetic data engineer?

The salary of a synthetic data engineer typically ranges from $80,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in data modeling, machine learning, and programming languages like Python or SQL tend to earn higher salaries.

Which 3 jobs will survive AI?

Synthetic Data Generation specialists are likely to continue being in demand as AI development requires high-quality, labeled data for training models. Roles involving data curation, domain expertise, and oversight of AI systems—such as data scientists, AI ethics officers, and machine learning engineers—are also expected to persist due to their specialized skills and the need for human judgment. These jobs often require technical knowledge, programming skills, and continuous learning to adapt to evolving AI technologies.

What is an example of synthetic data generation?

Synthetic data generation, relevant to roles like data scientists or AI engineers, involves creating artificial data that mimics real datasets using algorithms such as generative adversarial networks (GANs) or statistical models. For example, generating realistic customer transaction records for testing machine learning models without exposing sensitive information. This process helps improve model training while maintaining data privacy and security.

What is synthetic data generation?

Synthetic data generation is the process of creating artificial datasets that mimic real-world data. This technique is used to supplement or replace actual data for purposes such as machine learning, software testing, and research, especially when real data is scarce, sensitive, or costly to obtain. Synthetic data can help improve model accuracy, protect privacy, and enable innovation by providing diverse and unbiased datasets. It is commonly used in fields like healthcare, finance, and autonomous vehicles.

What is the difference between Synthetic Data Generation vs Data Analyst?

AspectSynthetic Data GenerationData Analyst
Required CredentialsKnowledge of data science, programming, and data privacyDegree in statistics, data science, or related field
Work EnvironmentData science teams, research labs, tech companiesBusiness environments, analytics teams, consulting firms
Industry UsageAI development, machine learning, data privacyBusiness insights, reporting, decision-making
Search & Comparison IntentUnderstanding data generation techniques, privacy solutionsAnalyzing data, generating reports, insights

While Synthetic Data Generation focuses on creating artificial data for privacy and model training, Data Analysts interpret existing data to provide business insights. Both roles require data-related skills but serve different purposes within the data ecosystem.

What are the main challenges faced by professionals working in synthetic data generation, and how can they be addressed?

Professionals in synthetic data generation often encounter challenges such as ensuring the generated data accurately represents real-world scenarios while maintaining privacy and data security. Balancing realism with anonymization is crucial, especially when synthetic data is used for AI model training or testing. Collaboration with data scientists, domain experts, and privacy officers is common to validate data utility and compliance with regulations. Staying current with advances in generative models and data validation techniques also helps address these challenges and contributes to career growth in this rapidly evolving field.

Is 40 too late for data science?

Age is not a barrier to entering data science or synthetic data generation roles. Many professionals successfully transition into these fields later in life by acquiring relevant skills such as programming, statistics, and machine learning, often through online courses or certifications. Experience, continuous learning, and adaptability are valued more than age in the tech industry.
More about Synthetic Data Generation jobs
What cities are hiring for Synthetic Data Generation jobs? Cities with the most Synthetic Data Generation job openings:
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What job categories do people searching Synthetic Data Generation jobs look for? The top searched job categories for Synthetic Data Generation jobs are:
Infographic showing various Synthetic Data Generation job openings in the United States as of June 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 66% Physical, 2% Hybrid, and 32% Remote job distribution, with an average salary of $93,198 per year, or $44.8 per hour.
Senior Software Engineer - Simulation

Senior Software Engineer - Simulation

NVIDIA

Santa Clara, CA • On-site

$143K - $189K/yr

Full-time

Posted 15 days ago


Job description

Job Summary:
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. They are seeking a highly motivated and experienced Senior Software Engineer to join their Metropolis Synthetic Data Generation team to build scalable Physical AI Digital Twin and Synthetic Data Generation solutions.
Responsibilities:
• Build, develop, and maintain scalable 3D simulation software for Digital Twin and Synthetic Data Generation applications.
• Collaborate with multi-functional teams to build backend services and AI Agents to create end-to-end SDG solutions.
• Implement scalable and resilient solutions for distributed computing environments.
• Optimize the performance and reliability of cloud applications and services.
• Develop user interfaces and frontend components as needed.
• Work closely with product managers to define and prioritize features and requirements.
• Participate in code reviews, build discussions, and team meetings.
• Stay up-to-date with industry trends and guidelines to ensure our solutions remain innovative.
Qualifications:
Required:
• Bachelor or higher degree in computer science, engineering, or equivalent experience.
• 12+ years of industrial experience in large-scale software development in Computer Graphics, Game Engine, or 3D Simulation.
• Excellent programming skills in languages such as C/C++, Python, and scripting languages.
• Proficiency in 3D simulation of one or more physics phenomena (e.g., rigid-body dynamics, fluid dynamics, material fracture, combustion, audio synthesis, and propagation).
• Proficiency in physics Game engines (e.g., Unreal, Unity, Chrono, Mujoco).
• Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).
• Excellent problem-solving skills and attention to detail.
• Ability to work effectively in a fast-paced, collaborative environment.
Preferred:
• Experience with NVIDIA GPU technology, Omniverse programming and developing AI agents.
• Experience with content generation using LLM and Generative AI models.
• Hands-on experience with 3D virtual content creation and animation tools (e.g., Maya, Blender, Houdini).
• Understanding of DevOps principles and practices in Cloud environments.
Company:
NVIDIA is a computing platform company operating at the intersection of graphics, HPC, and AI. Founded in 1993, the company is headquartered in Santa Clara, USA, with a team of 10001+ employees. The company is currently Late Stage.

Nvidia logo

About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993